In this course we will be covering the basics of X-Ray CT acquisition and reconstruction. We will introduce the filtered back projection (FBP) algorithm and the cone beam variant (FDK), which are the most widely used algorithms for reconstruction of XCT data. We will discuss the family of iterative reconstruction algorithms with regularisation that can be employed when FBP/FDK do not lead to satisfactory results.


The course will make use of the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging, developed by the Collaborative Computational Project in Tomographic Imaging (CCPi) and available from https://www.ccpi.ac.uk/cil. CIL provides FBP/FDK, state-of-the-art iterative reconstruction algorithms, pre-processing and visualization tools as well as readers for some of the common micro-CT data formats. The course will make use of an online platform where all the relevant software and data is pre-installed, but details on how to install CIL will be provided.

In this half-day hands-on training course you will:

--learn to load in and process micro-CT data (e.g. Nikon and Zeiss);
--produce and display your very own reconstructions using CIL;
--appreciate the pros and cons of iterative reconstruction when compared to filtered back-projection; and
--see different iterative reconstruction algorithms.

This two-part six-hour workshop will provide a demonstration of CT data processing tasks in 3D Slicer and SlicerMorph. 3D Slicer is a free, open-source software for visualization, processing, segmentation, registration, and analysis of biological, geological, medical, biomedical, and
other 3D images and meshes. SlicerMorph is a 3D Slicer extension for measurement, annotation, and geometric morphometric analysis on 3D data.

This workshop aims to equip attendees with the knowledge and tools needed to process and analyze CT datasets. This is an interactive workshop structured to guide participants through CT processing workflows. It will be presented in two sessions. The morning session will cover data import, 3D rendering/visualization, segmentation, and mesh generation.

Session 1 Topics: Visualization, segmentation, and meshes
● Introduction: what CT data are and where to find them
● Loading data: different file formats have different import protocols
● Segmentation: defining regions of interest in your CT scans
● Visualization: generating 3D rendered images of CT scans
● Meshes: creating 3D models using CT data

This two-part six-hour workshop will provide a demonstration of CT data processing tasks in 3D Slicer and SlicerMorph. 3D Slicer is a free, open-source software for visualization, processing, segmentation, registration, and analysis of biological, geological, medical, biomedical, and
other 3D images and meshes. SlicerMorph is a 3D Slicer extension for measurement, annotation, and geometric morphometric analysis on 3D data.

This workshop aims to equip attendees with the knowledge and tools needed to process and analyze CT datasets. This is an interactive workshop structured to guide participants through CT processing workflows. It will be presented in two sessions. The afternoon session will cover quantification and characterization, including linear and volumetric measurements, landmark placement, and geometric morphometric analyses.

Session 2 Topics: Collecting quantitative data and characterization
● Markups: collecting landmarks and curve data, as well as basic measurements
● SlicerMorph: using multiple tools to analyze 3D data for geometric morphometrics
● Shape Analysis: Generalized Procrustes Analysis/ Principal Component Analysis
● ALPACA: automation of landmark placement for large datasets

In this interactive dual-session workshop you will explore Dragonfly 3D World, a
powerful software for image processing and analysis of CT data. Whether you are
new to the field or looking to sharpen your skills, this workshop will take you from
essential tools and interface basics to advanced AI-driven methods for
segmentation.
 
What to expect in Session 1: 
 
• Recommendation on hardware
• From loading data to stunning 3D renderings
• Introduction to segmentation
• Computing measurements and visualizing results
• Generating and exporting meshes

What You Need:
Bring your laptop with an internet connection—we’ll take care of the rest! Get
ready to explore, practice, and enhance your imaging workflow in a dynamic learning
environment.
Don’t miss this chance to level up your CT image analysis skills!

You've mastered the basics of Dragonfly 3D World and want to take your skills to the
next level! In this session, we'll dive deeper into advanced image processing
techniques, focusing on AI-powered segmentation and optimization strategies to
streamline your workflow. 
 
What to expect in Session 2: 
 
• Segmentation Wizard & AI-powered segmentation
• Selecting and training a deep learning model
• Creating a universal deep learning model and sharing it
• Hands-off: automation and batch processing

What You Need:
Bring your laptop with an internet connection and be ready to explore new
possibilities! This session is designed to build on Session 1, but even if you’re
joining fresh, you’ll gain valuable insights into high-level imaging analysis.
Get ready to push the boundaries of what’s possible with Dragonfly 3D World!

In this course we will be covering the basics of X-Ray CT acquisition and reconstruction. We will introduce the filtered back projection (FBP) algorithm and the cone beam variant (FDK), which are the most widely used algorithms for reconstruction of XCT data. We will discuss the family of iterative reconstruction algorithms with regularisation that can be employed when FBP/FDK do not lead to satisfactory results.


The course will make use of the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging, developed by the Collaborative Computational Project in Tomographic Imaging (CCPi) and available from https://www.ccpi.ac.uk/cil. CIL provides FBP/FDK, state-of-the-art iterative reconstruction algorithms, pre-processing and visualization tools as well as readers for some of the common micro-CT data formats. The course will make use of an online platform where all the relevant software and data is pre-installed, but details on how to install CIL will be provided.

In this half-day hands-on training course you will:

--learn to load in and process micro-CT data (e.g. Nikon and Zeiss);
--produce and display your very own reconstructions using CIL;
--appreciate the pros and cons of iterative reconstruction when compared to filtered back-projection; and
--see different iterative reconstruction algorithms.

Join us for an interactive "Ask the Expert" workshop. This is your opportunity to get answers to all your questions about Avizo 3D visualization and analysis software, directly from our experts. Whether you're a seasoned user or new to Avizo Software, this workshop is designed to help you unlock the full potential of this powerful tool. You already use Avizo Software? Bring your data to our expert to get pro-tips on how to process them.

The workshop introduces two of the most effective techniques to perform 3D segmentation on CT data. First, we will demonstrate how to perform marker-based watershed segmentation. Finally, we will showcase the capabilities of Avizo Software's AI-powered segmentation tools.
Hands on. Certificates provided.

Join us in this workshop to explore different tools and techniques that make working with CT data simple and fun. Aimed at people eager to learn how to perform segmentation of complex structures intuitively and quickly. Gain in-depth knowledge of the current advanced methods used across the industries. Go beyond just visualization and learn how to get meaningful analytical results from your CT data with VGSTUDIO MAX.

What to expect:

CT basics
VGSTUDIO MAX user interface and data handling
Gray value-based Segmentation
Shape-based Segmentation
Paint & Segment (machine-learning optimized segmentation).
Deep-Segmentation (deep-learning optimized segmentation).
Analyses & Results
Automation

What you need:
Laptop with an internet connection and a creative mindset.

Hardware requirements:
GPU Minimum: A dedicated built-in NVIDIA or AMD graphics card with at least 2 GB VRAM, OpenGL 3.3 support, and—for Windows operating systems—the latest WHQL driver.
CPU Minimum: x86-64 CPU with instruction set SSE 4.1. [ARM processors not supported].
RAM Minimum: 4GB.
Operating System: Windows 10/11 [macOS not supported]